A search engine optimizer, which works independently and in parallel with a browser and search engine supercomputer to gather, analyze, and distill input information interactively. The optimizer reorganizes the input, and providing an optimized version as an output. The optimized version of the input (e.g. output) is sent to the search engine, which responds to the end user with search results. The optimizer recognizes each request as a pattern and stores the pattern in an advanced Glyph format. This permits the optimizer to identify a left and ride side check mate combination required to achieve certitude.
Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system including an internet search engine possessing a neural network of one or more subject matter data warehouses with a computer program that establishes a minimum word value associated with vague terms and a maximum word value associated with unique terms, wherein said vague terms are defined as terms having the highest quantity of occurrences of the term in the improved environment and unique terms are defined as terms having the lowest quantity of occurrences of the term in the improved environment, the program comprising instructions that when executed by the data warehouses cause the data warehouses to perform operations comprising: receiving the input as P(Q) (Probability P( ) given Query Q) from the interface as a query from a device; determining the terms P(T) (Probability P( ) given Term T)) as a list of semantically associated terms, hereinafter referred to as terms in at least one subject matter data warehouse, wherein a term is mapped to the query using conditional algebra events probabilities of the terms given the query; defining an improved environment upon removing from calculation irrelevant searchable Internet web pages using conditional algebra events probabilities of the terms given the query; validating each web page of the improved environment using the probability to exceed a predefined numeric threshold and upon a negative determination distilling those web pages; measuring the relative occurrence of each semantic associated term within the improved environment and assigning a quantitative value to each semantic associated term; identifying a set of results P(R) (Probability P( ) given results R) from the improved environment for the query in said linked database server device using conditional algebra events probabilities of the results given the terms; utilizing a probability of the query, is a function of a number of times the query was received relative to a history of queries received; validating each web page of the set of results using conditional algebra events probabilities of the results given the terms and upon a negative determination attenuating those web pages; ranking from highest to lowest in an order, the result among a set of results based on a relevance to the query to the improved environment; and sending the results to the end user client device.
This invention relates to an internet search engine system that improves search result relevance by leveraging neural networks and probabilistic modeling. The system addresses the challenge of distinguishing between vague and unique search terms to enhance query accuracy. It defines vague terms as those with the highest occurrence frequency in the search environment and unique terms as those with the lowest frequency. The system uses a neural network-based data warehouse to process queries by first mapping input terms to semantically associated terms using conditional probability calculations. It then refines the search environment by removing irrelevant web pages based on term probabilities, ensuring only relevant pages are considered. Each term's occurrence is quantified, and results are validated against a predefined threshold to filter out low-relevance pages. The system further ranks results by relevance, considering both term probabilities and query history. The final ranked results are then sent to the user. This approach improves search precision by dynamically adjusting term weights and filtering irrelevant content, ensuring more accurate and contextually relevant search outcomes.
2. The system of claim 1 , further comprising providing the results in the order of highest to lowest probability to the device.
A system for ranking and displaying search results based on probability is disclosed. The system addresses the problem of presenting search results in an unordered or irrelevant manner, which can frustrate users and reduce efficiency. The system processes input data, such as user queries or device-generated requests, and generates a set of results. Each result is assigned a probability score indicating its relevance or likelihood of being the correct or desired outcome. The system then ranks these results from highest to lowest probability, ensuring the most relevant results appear first. This ranking is dynamically adjusted based on real-time data, user behavior, or contextual factors to improve accuracy. The ranked results are then transmitted to a user device, such as a smartphone, tablet, or computer, for display. The system may also include additional features, such as filtering or categorizing results, to further enhance usability. By prioritizing results based on probability, the system improves search efficiency and user satisfaction.
3. The system of claim 1 , further comprising identifying at least one root word associated with a word included in the query and replacing the word included in the query with the root word.
This invention relates to a system for processing natural language queries, particularly for improving search accuracy by normalizing query terms. The system addresses the challenge of matching user queries to relevant content when variations of the same word (e.g., "running" vs. "run") or different word forms (e.g., plural vs. singular) exist. The system enhances query processing by identifying the root form of words in the query and replacing them with their base or root form. For example, if a user enters "runs" or "running," the system converts these to "run" before executing the search. This normalization step ensures that the query matches documents containing any form of the word, improving recall and precision. The system may also include preprocessing steps like tokenization and stemming to further refine the query. By standardizing word forms, the system reduces ambiguity and enhances the efficiency of information retrieval in databases, search engines, or other natural language processing applications. The invention is particularly useful in domains where linguistic variations can lead to missed matches, such as legal, medical, or technical documentation.
4. The system of claim 1 , further comprising in at least one subject matter data warehouse, analyzing each term in the improved environment based upon a set of predetermined semantic guidelines for a natural language and assigning a relative quantitative value to each of term derived from an analysis of search click data.
This invention relates to a system for analyzing and processing terms within a subject matter data warehouse using natural language processing and search click data. The system addresses the challenge of improving the accuracy and relevance of term analysis by incorporating semantic guidelines and quantitative valuation derived from user behavior. The system includes a data warehouse containing subject matter data, where each term within the environment is analyzed according to predefined semantic rules for a natural language. These rules ensure that the analysis aligns with linguistic and contextual standards. Additionally, the system assigns a relative quantitative value to each term based on an examination of search click data. This quantitative valuation reflects the term's relevance or importance as inferred from user interactions, such as search queries and click-through rates. By combining semantic analysis with behavioral data, the system enhances the precision of term interpretation and prioritization. This approach improves information retrieval, content categorization, and user experience in applications like search engines, recommendation systems, or knowledge management platforms. The integration of search click data ensures that the system adapts to real-world usage patterns, making it more effective in dynamic environments.
5. The system of claim 1 , further comprising determining in at least one subject matter data warehouse quantifying and qualifying the semantic structure of each term in the improved environment with a plurality of predefined individual word collections definitions, each semantic association word in a particular one of said word collections sharing a common assigned value.
This invention relates to a system for analyzing and structuring semantic relationships within a data environment. The system addresses the challenge of accurately quantifying and qualifying the meaning and context of terms in large datasets, particularly in subject matter data warehouses. The core functionality involves evaluating the semantic structure of each term by comparing it against predefined collections of words, where each collection contains words that share a common semantic association and an assigned value. This process helps standardize the interpretation of terms across different datasets, improving consistency in data analysis and retrieval. The system enhances the original data environment by integrating this semantic analysis, allowing for more precise categorization and relationship mapping between terms. The predefined word collections serve as reference points, ensuring that terms are evaluated within a structured framework, which can be customized based on specific domains or applications. This approach enables more accurate semantic search, data integration, and knowledge discovery in large-scale data repositories. The system is particularly useful in fields requiring high precision in language processing, such as legal, medical, or technical documentation analysis.
6. The system of claim 1 , further comprising validating each web pages of the improved environment using a conditional algebra event probability measuring a string similarity between the query and a string name domain topic; and upon a negative determination attenuating the ranking value of each web page.
A system for improving web search environments addresses the problem of ranking web pages based on relevance to a user's query. The system initially processes a query to identify relevant web pages and assigns ranking values to them. To enhance accuracy, the system validates each web page by measuring the string similarity between the query and the web page's domain topic using conditional algebra event probability. If the similarity is insufficient, the system attenuates (reduces) the ranking value of the web page. This ensures that pages with low relevance to the query are deprioritized in search results. The validation process helps filter out irrelevant or misleading content, improving the overall quality of search results. The system may also include additional features such as query expansion, where related terms are identified to broaden the search scope, and result clustering, where similar web pages are grouped to provide a more organized presentation. These enhancements collectively refine the search experience by prioritizing highly relevant content and reducing noise in the results.
7. A system including an internet search engine possessing a neural network of one or more subject matter data warehouses with a computer program that establishes a minimum word value associated with vague terms and a maximum word value associated with unique terms, wherein said vague terms are defined as terms having the highest quantity of occurrences of the term in the improved environment and unique terms are defined as terms having the lowest quantity of occurrences of the term in the improved environment, the program comprising instructions that when executed by the data warehouses cause the data warehouses to perform operations comprising: receiving the input as P(Q) (Probability P( ) given Query Q) from the interface as a query from a device; determining the terms P(T) (Probability P( ) given Term T)) as a list of semantically associated terms, hereinafter referred to as terms in at least one subject matter data warehouse, wherein a term is mapped to the query using conditional algebra events probabilities of the terms given the query; defining an improved environment upon removing from calculation irrelevant searchable Internet web pages using conditional algebra events probabilities of the terms given the query; validating each web page of the improved environment using the probability to exceed a predefined numeric threshold and upon a negative determination distilling those web pages; measuring the relative occurrence of each semantic associated term within the improved environment and assigning a quantitative value to each semantic associated term; identifying a set of results P(R) (Probability P( ) given results R) from the improved environment for the query in said linked database server device using conditional algebra events probabilities of the results given the terms; utilizing the semantic analysis of the terms given the query P(T), for validating each web page of the set of results using P(T) to exceed a predefined numeric threshold and upon a negative determination attenuating the ranking score of those web pages; ranking the result from highest to lowest in an order, among a set of results based on a relevance to the query to the improved environment; and sending the results to the end user client device.
This invention relates to an internet search engine system that enhances search accuracy by leveraging a neural network and subject matter data warehouses to refine query results. The system addresses the challenge of distinguishing between vague terms (high-frequency, low-specificity terms) and unique terms (low-frequency, high-specificity terms) to improve search relevance. A computer program assigns minimum word values to vague terms and maximum word values to unique terms, dynamically adjusting term significance based on their occurrence frequency in the search environment. The system processes queries by first mapping input terms to semantically associated terms within data warehouses using conditional probability models. It then filters out irrelevant web pages, creating an "improved environment" of relevant content. Each remaining web page is validated against a predefined probability threshold, with low-scoring pages removed. The system measures term occurrence frequency in this refined environment, assigning quantitative values to each term. Results are identified using conditional probabilities of results given the terms, and each result is validated against the same threshold. Low-scoring results are downranked. Finally, results are ranked by relevance and sent to the user. The neural network and data warehouses enable dynamic term weighting and probabilistic filtering, improving search precision by reducing noise from vague terms while prioritizing unique, contextually relevant terms.
8. The system of claim 7 , further comprising ranking the result from highest to lowest in an order sending the output to the end user client device.
A system for processing and ranking search results is disclosed. The system operates in the domain of information retrieval and user interface optimization, addressing the problem of efficiently presenting relevant search results to users in a prioritized manner. The system receives a query from an end user client device and processes the query to generate a set of search results. These results are then ranked from highest to lowest relevance or priority, with the highest-ranked results sent to the end user client device for display. The ranking may be based on factors such as relevance, recency, user preferences, or other criteria. The system ensures that the most pertinent results are presented first, improving user experience by reducing the time and effort required to find desired information. The ranking mechanism may involve algorithms that analyze the content of the results, user behavior data, or other contextual information to determine the optimal order. The system may also include additional features such as filtering or categorizing results before ranking to further refine the output. By dynamically adjusting the ranking based on real-time data, the system adapts to user needs and preferences, enhancing the efficiency and effectiveness of information retrieval.
9. The system of claim 7 , further comprising identifying at least one root word associated with a word included in the query and replacing the word included in the query with the root word.
This invention relates to natural language processing and query refinement in search systems. The problem addressed is improving search accuracy by normalizing query terms to their root forms, which helps match variations of words that convey the same meaning. The system processes a user's search query by identifying at least one root word associated with a word in the query and replacing the original word with this root form. This step ensures that different inflections, plurals, or derivatives of a word are treated as equivalent, enhancing search relevance. The system may also include a query expansion module that broadens the search by incorporating synonyms or related terms, and a ranking module that prioritizes results based on relevance to the refined query. The root word identification process may involve stemming or lemmatization techniques to determine the base form of words. This approach improves search efficiency by reducing the impact of morphological variations on query matching, leading to more accurate and comprehensive results. The system is particularly useful in applications where users may input queries with varying word forms, such as web search engines, document retrieval systems, or database queries.
10. The system of claim 7 , further comprising in at least one subject matter data warehouse, analyzing each term in the improved environment based upon a set of predetermined semantic guidelines for a natural language and assigning a relative quantitative value to each of term derived from an analysis of search click data.
This invention relates to a system for analyzing and quantifying terms in a subject matter data warehouse using natural language processing and search click data. The system addresses the challenge of extracting meaningful insights from unstructured text by leveraging semantic analysis and user interaction data to assign quantitative values to terms. The system includes a data warehouse containing subject matter data, which may include documents, records, or other textual information. A natural language processing module processes the text, identifying and analyzing each term according to predetermined semantic guidelines. These guidelines define linguistic rules, contextual relationships, and relevance criteria to ensure consistent interpretation of terms across the dataset. To enhance term analysis, the system incorporates search click data, which reflects user behavior when interacting with the data warehouse. This data helps quantify the importance or relevance of terms by measuring how often they are clicked, searched, or otherwise engaged with by users. The system assigns a relative quantitative value to each term based on this analysis, enabling prioritization, categorization, or further processing of the data. The system may also include a user interface for displaying the analyzed terms and their assigned values, allowing users to explore the data warehouse more effectively. The quantitative values can be used for ranking search results, improving information retrieval, or identifying key topics within the dataset. By combining semantic analysis with real-world user interaction data, the system provides a more accurate and dynamic assessment of term relevance.
11. The system of claim 7 , further comprising determining in at least one subject matter data warehouse quantifying and qualifying the semantic structure of each term in the improved environment with a plurality of predefined individual word collections definitions, each semantic association word in a particular one of said word collections sharing a common assigned value.
The system is designed for semantic analysis within a data warehouse environment, addressing the challenge of accurately quantifying and qualifying the meaning and relationships of terms in large datasets. The system enhances data processing by analyzing the semantic structure of each term using predefined word collections, where each collection contains words with shared semantic associations and assigned common values. These collections help standardize the interpretation of terms across different datasets, improving consistency in data analysis. The system integrates with existing data warehouses to evaluate terms against these predefined collections, ensuring that semantic relationships are systematically captured and quantified. This approach allows for more precise data interpretation, enabling better decision-making and insights extraction from complex datasets. The predefined word collections serve as a reference framework, ensuring that terms are evaluated within a structured semantic context, reducing ambiguity and improving the accuracy of data-driven conclusions. The system is particularly useful in environments where consistent semantic interpretation is critical, such as natural language processing, knowledge management, and data analytics. By leveraging these predefined collections, the system automates the process of semantic qualification, making it scalable and adaptable to various data sources and applications.
12. The system of claim 7 , further comprising validating each web pages of the improved environment using a conditional algebra event probability measuring a string similarity between the query and a string name domain topic; and upon a negative determination attenuating the ranking value of each web page.
This invention relates to a system for improving web search environments by validating web pages based on conditional algebra event probability and string similarity analysis. The system addresses the problem of ranking irrelevant or low-quality web pages in search results by dynamically adjusting their ranking values. The system includes a search environment that processes user queries and retrieves web pages from a database. It further comprises a validation module that evaluates each web page by measuring the string similarity between the user's query and the domain topic of the web page. This measurement uses conditional algebra event probability to assess relevance. If the similarity is below a threshold, the system attenuates (reduces) the ranking value of the web page, thereby lowering its position in search results. The validation module operates by comparing the query string to the domain topic of each web page, which is a categorical label representing the page's subject matter. The conditional algebra event probability quantifies the likelihood that the query and domain topic are semantically related. If this probability falls below a predefined threshold, the system downgrades the page's ranking to improve search result quality. This approach enhances search accuracy by dynamically filtering out less relevant pages, ensuring that higher-ranking results are more closely aligned with the user's intent. The system can be integrated into existing search engines or web applications to improve the relevance of search outcomes.
13. A method including an internet search engine possessing a neural network of one or more subject matter data warehouses that establishes a minimum word value associated with vague terms and a maximum word value associated with unique terms, wherein said vague terms are defined as terms having the highest quantity of occurrences of the term in the improved environment and unique terms are defined as terms having the lowest quantity of occurrences of the term in the improved environment, the method comprising: receiving the input as P(Q) (Probability P( ) given Query Q) from the interface as a query from a device; determining the terms P(T) (Probability P( ) given Term T)) as a list of semantically associated terms, hereinafter referred to as terms in at least one subject matter data warehouse, wherein a term is mapped to the query using conditional algebra events probabilities of the terms given the query; validating each web page of the improved environment using the probability to exceed a predefined numeric threshold and upon a negative determination distilling those web pages; measuring the relative occurrence of each semantic associated term within the improved environment and assigning a quantitative value to each semantic associated term; identifying a set of results P(R) (Probability P( ) given results R) from the improved environment for the query in said linked database server device using conditional algebra events probabilities of the results given the terms; performing the semantic data analysis of the terms given the query to determine P(T) using at least one knowledge database; validating each web page of the set of results using conditional algebra events probabilities of the results given the terms and upon a negative determination attenuating the ranking score of those web pages; ranking from highest to lowest in an order, the result among a set of results based on a relevance to the query to the improved environment; and sending the results to the end user client device.
This invention relates to an internet search engine system that improves search result relevance by analyzing term frequency and semantic associations within a neural network-based data warehouse. The system addresses the challenge of distinguishing between vague terms (high occurrence frequency) and unique terms (low occurrence frequency) to enhance search accuracy. The neural network assigns minimum word values to vague terms and maximum word values to unique terms, refining the search process. The method begins by receiving a user query and determining semantically associated terms from one or more subject matter data warehouses. Each term is mapped to the query using conditional probability calculations. Web pages are validated based on their probability of exceeding a predefined threshold, with low-probability pages being filtered out. The system then measures the relative occurrence of each semantic term within the search environment, assigning quantitative values to them. Results are identified using conditional probability calculations of results given the terms, followed by semantic data analysis to refine term probabilities. Each result is validated again, and low-probability pages have their ranking scores reduced. The final results are ranked by relevance to the query and sent to the user. This approach improves search precision by leveraging semantic relationships and term frequency analysis within a structured data warehouse.
14. The method of claim 13 , further comprising providing the results in the order of highest to lowest probability to the device.
A system and method for ranking and presenting search results based on probability. The technology addresses the challenge of efficiently organizing and displaying search results to users, particularly in scenarios where multiple potential matches exist with varying degrees of relevance. The method involves processing input data, such as user queries or device-generated requests, to generate a set of potential results. Each result is assigned a probability score indicating its likelihood of being the correct or most relevant match. The results are then sorted in descending order of probability, from highest to lowest, to prioritize the most likely matches for the user. This ranking ensures that the most probable results are presented first, improving user experience by reducing the time and effort required to find the desired information. The method may be applied in various domains, including search engines, recommendation systems, and automated data retrieval processes, where accurate and efficient result presentation is critical. The system may also include additional features, such as filtering or refining the results based on user preferences or contextual data, to further enhance the accuracy and relevance of the presented information.
15. The method of claim 13 , further comprising identifying at least one root word associated with a word included in the query and replacing the word included in the query with the root word.
This invention relates to natural language processing and query refinement in search systems. The problem addressed is improving search accuracy by normalizing query terms to their root forms, which helps match variations of words that convey the same meaning. The method involves analyzing a search query to identify at least one word within it, determining a root word (such as a lemma or stem) associated with that word, and replacing the original word in the query with its root form. This step is part of a broader process that includes receiving a search query, processing the query to extract relevant terms, and refining those terms to enhance search performance. By converting words to their root forms, the system ensures that different inflections, plurals, or verb conjugations of the same word are treated as equivalent, improving the precision and recall of search results. The technique is particularly useful in applications where users may input queries in various linguistic forms, such as search engines, recommendation systems, or question-answering platforms. The method may also involve additional preprocessing steps like tokenization, part-of-speech tagging, or synonym expansion to further refine the query before execution. The overall goal is to enhance the relevance of search results by standardizing query terms to their most fundamental linguistic representations.
16. The method of claim 13 , further comprising in at least one subject matter data warehouse, analyzing each term in the improved environment based upon a set of predetermined semantic guidelines for a natural language and assigning a relative quantitative value to each of term derived from an analysis of search click data.
This invention relates to natural language processing and data analysis in subject matter data warehouses. The problem addressed is the need to improve the semantic understanding and quantitative evaluation of terms within a data warehouse by leveraging search click data. The method involves analyzing each term in the data warehouse according to predetermined semantic guidelines for a natural language. These guidelines define how terms should be interpreted and evaluated. The analysis includes examining search click data, which reflects user behavior and preferences when interacting with search results. Based on this analysis, a relative quantitative value is assigned to each term, representing its relevance, importance, or other measurable attribute within the context of the data warehouse. This quantitative evaluation helps enhance the accuracy and effectiveness of search and retrieval operations, improving the overall usability of the data warehouse. The method ensures that terms are not only semantically analyzed but also dynamically evaluated based on real-world usage patterns captured in search click data. This approach enables more precise and context-aware data processing, benefiting applications such as information retrieval, content recommendation, and knowledge management.
17. The method of claim 13 , further comprising determining in at least one subject matter data warehouse quantifying and qualifying the semantic structure of each term in the improved environment with a plurality of predefined individual word collections definitions, each semantic association word in a particular one of said word collections sharing a common assigned value.
This invention relates to semantic analysis in data warehousing, specifically improving the accuracy of term interpretation by quantifying and qualifying their semantic structure. The problem addressed is the ambiguity and inconsistency in term definitions across different data sources, which leads to misinterpretation and poor data integration. The solution involves analyzing terms within a data warehouse by comparing them against predefined word collections, where each collection contains semantically associated words sharing a common assigned value. This process ensures that terms are consistently interpreted based on their semantic relationships, enhancing data accuracy and reliability. The method includes storing these word collections in a structured format, such as a database or lookup table, and applying them to evaluate the semantic structure of terms in the data warehouse. By mapping terms to these predefined collections, the system can assign standardized meanings, reducing ambiguity and improving data consistency. This approach is particularly useful in environments where multiple data sources with varying terminologies need to be integrated, such as enterprise data warehouses or knowledge management systems. The invention ensures that terms are interpreted uniformly, leading to more reliable analytics and decision-making.
18. The method of claim 13 , further comprising validating each web pages of the improved environment using a conditional algebra event probability measuring a string similarity between the query and a string name domain topic; and upon a negative determination attenuating the ranking value of each web page.
This invention relates to web search systems that improve search results by validating web pages in an improved search environment. The problem addressed is ensuring the relevance and accuracy of search results by measuring the similarity between a user's query and the content of web pages. The method involves analyzing web pages in an improved search environment, where the environment may include enhanced indexing, ranking, or other modifications to standard search techniques. For each web page, the system measures the probability of a match between the query and the web page's content using conditional algebra event probability. This probability is based on string similarity, which assesses how closely the query aligns with the web page's domain or topic. If the similarity is insufficient, the system reduces the ranking value of the web page, effectively lowering its position in search results. This ensures that only highly relevant pages are prioritized. The method may also include additional steps such as refining the search environment, adjusting ranking algorithms, or filtering out low-quality pages. The goal is to enhance search accuracy and user satisfaction by dynamically validating and ranking web pages based on their relevance to the query.
19. A system including an internet search engine possessing a neural network of one or more subject matter data warehouses with a computer program, the program comprising instructions that when executed by the data warehouses cause the data warehouses to perform operations comprising: receiving the input as P(Q) (Probability P( ) given Query Q) from the interface as a query from a device; determining the terms P(T) (Probability P( ) given Term T)) as a list of semantically associated terms, hereinafter referred to as terms in at least one subject matter data warehouse, wherein a term is mapped to the query using conditional algebra events probabilities of the terms given the query; defining an improved environment upon removing from calculation irrelevant searchable Internet web pages using conditional algebra events probabilities of the terms given the query; measuring the relative occurrence of each semantic associated term within the improved environment and assigning a quantitative value to each semantic associated term; identifying a set of results P(R) (Probability P( ) given results R) from the improved environment for the query in said linked database server device using conditional algebra events probabilities of the results given the terms; performing the semantic data analysis of the terms given the query to determine P(T) using at least one knowledge database and physically parsing and data mining the results to be displayed to the end user using P(T) to exceed a predefined numeric threshold and upon a negative determination attenuating the ranking score of those web pages; and sending in an order from highest to lowest web pages based on the ranking score to the end user client device.
This system enhances internet search engines by using a neural network and multiple subject matter data warehouses to improve search result relevance. The system addresses the problem of irrelevant or low-quality search results by leveraging conditional probability and semantic analysis. When a user submits a query, the system processes the input as a probability distribution given the query (P(Q)). It then determines a list of semantically associated terms (P(T)) by mapping terms to the query using conditional probability events, filtering out irrelevant web pages to create an improved search environment. The system measures the relative occurrence of each semantic term within this refined environment and assigns quantitative values to them. It then identifies a set of results (P(R)) from the improved environment by applying conditional probability to the terms, performing semantic data analysis to refine the results. The analysis uses at least one knowledge database to parse and mine the results, ensuring they meet a predefined relevance threshold. If the threshold is not met, the system attenuates the ranking score of those web pages. Finally, the system sends the ranked web pages, ordered from highest to lowest based on their ranking score, to the end user's device. This approach improves search accuracy by dynamically adjusting relevance based on semantic associations and probabilistic modeling.
20. A method including an internet search engine possessing a neural network of one or more subject matter data warehouses with a computer program, the method comprising: receiving the input as P(Q) (Probability P( ) given Query Q) from the interface as a query from a device; determining the terms P(T) (Probability P( ) given Term T)) as a list of semantically associated terms, hereinafter referred to as terms in at least one subject matter data warehouse, wherein a term is mapped to the query using conditional algebra events probabilities of the terms given the query; defining an improved environment upon removing from calculation irrelevant searchable Internet web pages using conditional algebra events probabilities of the terms given the query; measuring the relative occurrence of each semantic associated term within the improved environment and assigning a quantitative value to each semantic associated term; identifying a set of results P(R) (Probability P( ) given results R) from the improved environment for the query in said linked database server device using conditional algebra events probabilities of the results given the terms; performing the semantic data analysis of the terms given the query to determine P(T) using at least one knowledge database and physically parsing and data mining the results to be displayed to the end user using P(T) to exceed a predefined numeric threshold and upon a negative determination attenuating the ranking score of those web pages; and sending in an order from highest to lowest web pages based on the ranking score to the end user client device.
This invention relates to an internet search engine system that enhances search results using a neural network and subject matter data warehouses. The system addresses the problem of irrelevant or low-quality search results by leveraging semantic analysis and probabilistic modeling to refine query processing. The search engine receives a user query and determines a list of semantically associated terms by calculating conditional probabilities of terms given the query. These terms are mapped to the query using probabilistic models stored in one or more subject matter data warehouses. The system then filters out irrelevant web pages by recalculating probabilities in an improved search environment, measuring the relative occurrence of each term, and assigning quantitative values. Results are identified based on the refined probabilities, and semantic data analysis is performed to further refine the ranking. The system parses and mines the results, adjusting ranking scores for web pages that do not meet a predefined threshold. Finally, the search results are ordered by ranking score and sent to the user's device. The method ensures higher relevance by dynamically adjusting probabilities and filtering out low-quality content.
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June 7, 2016
March 22, 2022
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